A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists

User-generated item lists are a popular feature of many different platforms. Examples include lists of books on Goodreads, playlists on Spotify and YouTube, collections of images on Pinterest, and lists of answers on question-answer sites like Zhihu. Recommending item lists is critical for increasing user engagement and connecting users to new items, but many approaches are designed for the item-based recommendation, without careful consideration of the complex relationships between items and lists. Hence, in this paper, we propose a novel user-generated list recommendation model called AttList. Two unique features of AttList are careful modeling of (i) hierarchical user preference, which aggregates items to characterize the list that they belong to, and then aggregates these lists to estimate the user preference, naturally fitting into the hierarchical structure of item lists; and (ii) item and list consistency, through a novel self-attentive aggregation layer designed for capturing the consistency of neighboring items and lists to better model user preference. Through experiments over three real-world datasets reflecting different kinds of user-generated item lists, we find that AttList results in significant improvements in NDCG, Precision@k, and Recall@k versus a suite of state-of-the-art baselines. Furthermore, all code and data are available at https://github.com/heyunh2015/AttList.Comment: Accepted by CIKM 201

Joint Learning of Local and Global Features for Aspect-based Sentiment Classification

Aspect-based sentiment classification (ASC) aims to judge the sentiment polarity conveyed by the given aspect term in a sentence. The sentiment polarity is not only determined by the local context but also related to the words far away from the given aspect term. Most recent efforts related to the attention-based models can not sufficiently distinguish which words they should pay more attention to in some cases. Meanwhile, graph-based models are coming into ASC to encode syntactic dependency tree information. But these models do not fully leverage syntactic dependency trees as they neglect to incorporate dependency relation tag information into representation learning effectively. In this paper, we address these problems by effectively modeling the local and global features. Firstly, we design a local encoder containing: a Gaussian mask layer and a covariance self-attention layer. The Gaussian mask layer tends to adjust the receptive field around aspect terms adaptively to deemphasize the effects of unrelated words and pay more attention to local information. The covariance self-attention layer can distinguish the attention weights of different words more obviously. Furthermore, we propose a dual-level graph attention network as a global encoder by fully employing dependency tag information to capture long-distance information effectively. Our model achieves state-of-the-art performance on both SemEval 2014 and Twitter datasets.Comment: under revie

1,2-Bis(2-chloro­benzyl­idene)hydrazine

The title Schiff base compound, C14H10Cl2N2, crystallizes with one half-mol­ecule in the asymmetric unit. The mid-point of the N—N bond [1.418 (3) Å] lies on an inversion centre. The mol­ecular skeleton is approximately planar, the largest deviation from the mean plane being 0.143 (4) Å for the N-bonded C atom. The crystal packing exhibits no classical inter­molecular hydrogen bonds

Dependence of the decoherence of polarization states in phase-damping channels on the frequency spectrum envelope of photons

We consider the decoherence of photons suffering in phase-damping channels. By exploring the evolutions of single-photon polarization states and two-photon polarization-entangled states, we find that different frequency spectrum envelopes of photons induce different decoherence processes. A white frequency spectrum can lead the decoherence to an ideal Markovian process. Some color frequency spectrums can induce asymptotical decoherence, while, some other color frequency spectrums can make coherence vanish periodically with variable revival amplitudes. These behaviors result from the non-Markovian effects on the decoherence process, which may give rise to a revival of coherence after complete decoherence.Comment: 7 pages, 4 figures, new results added, replaced by accepted versio

2-[4-(4-Methylphenylsulfonyl)piperazin-1-yl]-1-(4,5,6,7-tetrahydrothieno[3,2-c]pyridin-5-yl)ethanone

In the title thienopyridine derivative, C20H25N3O3S2, the piperazine ring exhibits a chair conformation and the tetra­hydro­pyridine ring exhibits a half-chair conformation. The folded conformation of the mol­ecule is defined by the N—C—C—N torsion angle of −70.20 (2) °. Inter­molecular C—H⋯S and C—H⋯O hydrogen bonds help to establish the packing

1,1′-Methyl­enedipyridinium dichloride monohydrate

In the crystal structure of the title salt, C11H12N2 2+·2Cl−·H2O, the dication adopts a butterfly shape [dihedral angle between rings = 69.0 (1)°] with the water mol­ecule lying in the V-shaped cavity. Each O—H bond of the water molecule lies parallel to an aromatic ring and forms an O—H⋯Cl inter­action to a chloride anion. The methyl­ene C atom in the dication and the water O atoms lie on special positions of twofold site symmetry